As monitoring cameras get increasingly popular, requirements of performing security monitoring on important scenes through cameras also become more and more ubiquitous. The most urgent one among security monitoring requirements is monitoring and warning with regard to abnormal accumulation of people. If a density of people flow in a scene is too high, there is a risk of occurrence of dangerous accidents such as stampede. Thus, monitoring and predicting with regard to people density and crowd flow in a monitoring scene has quite important application value for city security.
Traditional security monitoring mainly monitors each camera manually, but with exponential growth of an amount of cameras, it will consume a lot of human resources. In addition, artificial determination criteria may depend on subjective experience, it is impossible to accurately quantify a current congestion degree and thereby make a right decision. Therefore, automatically determining a congestion degree in each scene by a machine intelligence system has very high value. However, traditional crowd congestion determining algorithms often are subjected to specific scenes and depend on view angle transformation in specific scenes as well as background modeling and geometry information in scenes. When it needs to replace a scene, re-adaptation is needed, so a monitoring model trained for specific scenes has no extendibility.